AgentSkillsCN

firebase-vertex-ai

执行Firebase平台专家，与Vertex AI Gemini集成进行认证、Firestore、存储、函数、托管和AI驱动功能。在被要求“设置firebase”、“部署到firebase”或“集成vertex ai与firebase”时使用。根据技能目的触发相关短语。

SKILL.md
--- frontmatter
name: firebase-vertex-ai
description: |
  Execute firebase platform expert with Vertex AI Gemini integration for Authentication, Firestore, Storage, Functions, Hosting, and AI-powered features. Use when asked to "setup firebase", "deploy to firebase", or "integrate vertex ai with firebase". Trigger with relevant phrases based on skill purpose.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT

Firebase Vertex AI

Operate Firebase projects end-to-end (Auth, Firestore, Functions, Hosting) and integrate Gemini/Vertex AI safely for AI-powered features.

Overview

Use this skill to design, implement, and deploy Firebase applications that call Vertex AI/Gemini from Cloud Functions (or other GCP services) with secure secrets handling, least-privilege IAM, and production-ready observability.

Prerequisites

  • •Node.js runtime and Firebase CLI access for the target project
  • •A Firebase project (billing enabled for Functions/Vertex AI as needed)
  • •Vertex AI API enabled and permissions to call Gemini/Vertex AI from your backend
  • •Secrets managed via env vars or Secret Manager (never in client code)

Instructions

  1. •Initialize Firebase (or validate an existing repo): Hosting/Functions/Firestore as required.
  2. •Implement backend integration:
    • •add a Cloud Function/HTTP endpoint that calls Gemini/Vertex AI
    • •validate inputs and return structured responses
  3. •Configure data and security:
    • •Firestore rules + indexes
    • •Storage rules (if applicable)
    • •Auth providers and authorization checks
  4. •Deploy and verify:
    • •deploy Functions/Hosting
    • •run smoke tests against deployed endpoints
  5. •Add ops guardrails:
    • •logging/metrics
    • •alerting for error spikes
    • •basic cost controls (budgets/quotas) where appropriate

Output

  • •A deployable Firebase project structure (configs + Functions/Hosting as needed)
  • •Secure backend code that calls Gemini/Vertex AI (with secrets handled correctly)
  • •Firestore/Storage rules and index guidance
  • •A verification checklist (local + deployed) and CI-ready commands

Error Handling

  • •Auth failures: identify the principal and missing permission/role; fix with least privilege.
  • •Billing/API issues: detect which API or quota is blocking and provide remediation steps.
  • •Firestore rule/index problems: provide minimal repro queries and rule fixes.
  • •Vertex AI call failures: surface model/region mismatches and add retries/backoff for transient errors.

Examples

Example: Gemini-backed chat API on Firebase

  • •Request: “Deploy Hosting + a Function that powers a Gemini chat endpoint.”
  • •Result: /api/chat function, Secret Manager wiring, and smoke tests.

Example: Firestore-powered RAG

  • •Request: “Build a RAG flow that embeds docs and answers with citations.”
  • •Result: ingestion plan, embedding + index strategy, and evaluation prompts.

Resources